When respondents who strongly prefer the public sector are presented with this data but without sector information, 82 percent select school B as best performing. However, when told that the school is private, only 19 percent select this school as best performing. Simply revealing that the provider of the school is private causes many elected officials to set aside their goal expectations for student well-being. In order to justify a desired conclusion (that a public school performs better than a private one), decision-makers reprioritize their evaluative criteria by lowering the importance they place on student well-being while redirecting the perceived importance to academic achievement instead. And similarly, the tendency to find well-being important becomes even stronger when a public school is shown to perform well on student well-being and less well on academic achievements. Thus, in group 4, no less than 94 percent of those who strongly prefer the public sector point to the public school as the school that performed best, compared to 71 percent who were not told what sector the school was in group 2.

5.
Why performance information use matters
Implies use of performance data about public action
◦ What do we really know about this?
◦ Mixed track record of performance management
◦ Not really sure how to do better
◦ Standard formula: improve the supply of data, or attach to incentives
◦ Can’t get far until we understand how people react to information

6.
Performance
information use is a
cognitive processes
Need to understand
what goes on inside our
heads when we are
given numbers

7.
Performance
information use is a
cognitive processes
To deal with complexity,
we use cognitive
shortcuts
Sometimes these
shortcuts are efficient
Sometimes they lead us
to make bad decisions –
they become biases
“It ain’t what you don’t know that gets you into trouble;
its what you know for sure that just ain’t so” – Mark Twain

8.
Control group – provides a
baseline
Treatment – the idea being
tested

9.
1. Numeric literacy: Do we even really like data…?
Public say they prefer statistical information over anecdote, but…
◦ Find anecdotal information more memorable, and more emotionally engaging
Left-most digit bias: round numbers are more compelling
Which school does better?
◦ School A: improved test scores from 6.1 to 6.9
◦ School B: improved test scores from 5.9-6.1

12.
2. Power of comparison
Comparative data: metric compared to peer, past, or future
◦ More persuasive
◦ Comparison with peers most compelling
◦ Setting unrealistic targets lowers evaluation of good performance
Provision of comparative data (how you ranked compared to other schools)
made principals more likely to download data
◦ Makes data more useful
◦ May be motivating

14.
3. Anti-public sector bias
“Citizens automatically and unconsciously associate public sector organizations
with inefficiency, inflexibility and other pejoratives, and these automatic
associations color their assessments of public sector performance” (Marvel
2016, 143).
In very different settings – US & Denmark – citizens rated public service
providers lower than private even when given exactly same performance data

15.
Implications of anti-public sector bias
Unclear: hide the role of the state or make it clearer
Complex in context where private actors play a big role delivering public
investments
Play up public-private partnerships

16.
4. Distrust of government data
Citizens trust self-reported public performance data less than data
provided by a third party
This distrust increases as the task becomes more complex

17.
Implications of distrust of data
Use third parties to collect and report on outcomes

18.
5. Motivated reasoning
Ideological beliefs alter how we select, weigh, interpret and use performance
data
We give credit to those we believe share out ideological beliefs and blame those
from other parties, e.g. Hurricane Katrina
Swiss legislators more likely to make use of data when it fits with their
ideological beliefs

19.
Motivated reasoning
US: Democrats and Republican citizens chose different performance data to
evaluate “Obamacare”
Exacerbates anti-public sector bias
◦ Conservative citizens and elected officials more likely to interpret same
performance data negatively if told service provider was public rather than
private
◦ Addition of more performance data did not debias elected official
assessments

20.
Affects who
elected officials
listen to
Comments by
teachers unions
criticizing test scores
reduced tendency by
liberal elected officials
to use the data, but
not for conservative
officials

21.
Applying motivated reasoning: Basic
concepts
How do different types of preferences relate to each other?
◦ Goal preferences: within a policy area, people care about different goals
◦ I think schools that perform better on student well-being rather than test scores are best
◦ Our goal preferences should guide how we evaluate public organizations
◦ Governance preferences: general beliefs about how government should be run, e.g.,
preference for public versus private provision
◦ I also prefer public schools over private

23.
Illustrative survey experiment
- 988 Danish city councillors (out of 2445  40.5 %).
- Asked to evaluate performance information regarding academic achievements and student well-being at
two schools: schools either do better on one goal or the other
- Asked about preference for public or private service provisions

25.
Implications of motivated reasoning
Performance data does not engender consensus where there is
are strong prior beliefs– may actually harden disagreement
Policymakers may ignore data on performance on issues they say
they care about if it challenges core beliefs
Policymakers “shop around” for data to justify core beliefs
Present data as neutrally as possible
◦Reflects competing values
◦Shows good and bad news
◦Get advance commitment from all parties on goals

26.
6. Negativity bias
Loss aversion
◦ People are more motivated by loss than equivalent gain
◦ We see this in how media chooses to cover public sector

28.
Negativity bias
We are more interested in and
responsive to data that is
labeled as low performance

29.
Negativity bias
Citizens evaluate services more negatively if the same performance data is
presented in a negative terms (rates of dissatisfaction) rather than a positive
(rates of satisfaction)
Asymmetry in how we give credit to good and bad performance
◦ Public more critical of UK local political incumbents and less likely to vote for them
when performance data is labeled as poor, but do not provide equivalent credit when
performance is good

30.
Negativity bias:
holding leaders accountable
Local elected officials asked to attribute responsibility for outcomes
of schools to school principals
Performance data increases responsibility attribution in cases of low
performance

31.
Implications of negativity bias
”We should have excellence in government” -
President Trump’s son-in-law
That’s not how our brains are wired
Manage to avoid failure rather than excellence
Present results in terms of levels of
achievement, not failure
Use data to identify risk and problem-solve

32.
Conclusion
Welcome comments and questions
How do these findings apply to your area of work?
Web: http://www.lafollette.wisc.edu/faculty-staff/faculty/donald-moynihan
Email: dmoynihan@lafollette.wisc.edu
Twitter: @donmoyn